CLASSIFICATION DEVICE, CLASSIFICATION METHOD, AND CLASSIFICATION PROGRAM
A classification device ( 10 ) includes: a classification unit ( 12 ) that performs classification by using a model ( 121 ) that is a model performing classification and is a deep learning model; and a preprocessing unit ( 11 ) that is provided prior to the classification unit ( 12 ), and selects an input to the model ( 121 ) by using a mask model ( 111 ) that minimizes a sum of a loss function and a magnitude of the input to the classification unit ( 12 ), the loss function evaluating a relationship between a label on an input from teaching data and an output of the model ( 121 ).
1 . A classification device, comprising:
a classifier configured to classify using a first model that is a model performing classification and includes a deep learning model; and
a preprocessor configured to, prior to the classifier classifying, select an input to the first model by using a second model that minimizes a sum of a loss function and a magnitude of the input to the classifier, the loss function evaluating a relationship between a label on an input from teaching data and an output of the first model.
2 . The classification device according to claim 1 , further comprising a learner configured to learn the teaching data and update parameters of the first model and the second model such that the sum of the loss function and the magnitude of the input to the classifier is minimized.
3 . The classification device according to claim 2 , wherein the learner determines a gradient of the loss function, by using an approximation of a Bernoulli distribution that is a probability distribution taking two values.
4 . A computer-implemented method for classifying, comprising:
classifying, by a classifier, using a first model that is a model performing classification and is a deep learning model; and
selecting, by a preprocessor, an input to the first model by using a second model that minimizes a sum of a loss function and a magnitude of the input to the classifier, the loss function evaluating a relationship between a label on an input from teaching data and an output of the first model, the preprocessor executing prior to the classifier.
5 . A computer-readable non-transitory recording medium storing computer-executable program instruction that when executed by a processor cause a computer system to:
classify, by a classifier, using a first model that is a model performing classification and is a deep learning model; and
selecting, by a preprocessor, an input to the first model by using a second model that minimizes a sum of a loss function and a magnitude of the input to the classifier, the loss function evaluating a relationship between a label on an input from teaching data and an output of the first model, the preprocessor executing prior to the classification step.
6 . The classification device according to claim 1 , wherein the second model used by the preprocessor includes a mask model masking the input based a correlation between the label on the input from teaching data and the output of the first model.
7 . The computer-implemented method according to claim 4 , the method further comprising:
learning, by a learner, the teaching data; and
updating, by the learner, parameters of the first model and the second model such that the sum of the loss function and the magnitude of the input to the classifier is minimized.
8 . The computer-implemented method according to claim 4 , wherein the second model used by the preprocessor includes a mask model masking the input based a correlation between the label on the input from teaching data and the output of the first model.
9 . The computer-readable non-transitory recording medium according to claim 5 , the computer-executable program instructions when executed further causing the computer system to:
learn, by a learner, the teaching data; and
update, by the learner, parameters of the first model and the second model such that the sum of the loss function and the magnitude of the input to the classifier is minimized.
10 . The computer-readable non-transitory recording medium according to claim 5 , wherein the second model used by the preprocessor includes a mask model masking the input based a correlation between the label on the input from teaching data and the output of the first model.
11 . The computer-implemented method according to claim 7 , wherein the learner determines a gradient of the loss function, by using an approximation of a Bernoulli distribution that is a probability distribution taking two values.
12 . The computer-readable non-transitory recording medium according to claim 9 , wherein the learner determines a gradient of the loss function, by using an approximation of a Bernoulli distribution that is a probability distribution taking two values.